A Local Contrast Method for Infrared Small-Target Detection Utilizing a Tri-Layer Window

By Jinhui Han, Saed Moradi, Iman Faramarzi, Chengyin Liu, Honghui Zhang, Qian Zhao. IEEE Geoscience and Remote Sensing Letters, 2020

441 citations recorded by the source corpus.

Local contrast has been proved efficient for infrared (IR) small-target detection. However, current algorithms do not enhance true target purposefully before local contrast calculation and may easily be disturbed by noises. In this letter, a new detection framework named multiscale tri-layer local contrast measure (TLLCM) is proposed. First, a tri-layer filtering window is proposed, and it consists of a core layer, a reserve layer, and a surrounding layer. The idea of a matched filter is adopted, and a Gaussian filtering will be performed on the core layer to enhance true target purposefully according to the target shape. Then, the multiscale TLLCM of the central pixel of the window will be calculated between the enhanced core and the surrounding local background. Finally, the target can be extracted by an adaptive threshold. Experimental results show that the proposed method can achieve better detection performance than some existing algorithms.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/f2b1243ea6fb8eba33681de5de2adfdc3893c0eb

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In Aerospace Engineering

1. Yulan Guo
2. Chao Xiao
3. Longguang Wang
4. Miao Li
5. Boyang Li
6. Wei An
7. Yingqian Wang
8. Zaiping Lin

In Electrical and Electronic Engineering

1. Wei An

Citations in the corpus

• Dense Nested Attention Network for Infrared Small Target Detection (2021, methodology): “[6], [9], local-contrast-based methods [10], [11], [12], [13],”